{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e782636b",
   "metadata": {},
   "source": [
    "# NOTE:\n",
    "This is based on the <b><tt>reflec-analysis.ipynb</tt></b>.\n",
    "To basic idea is similar to <b><tt>nite.mac</tt></b> at APS for non-disruptive, continuous data collection without human interference during overnight.\n",
    "\n",
    "SO the steps is as follows:\n",
    "- Initialize the analysis method/model. (universal to all data files)\n",
    "- Speficiy the bounds of parameter space for all. (universal to all data files)\n",
    "- Define Monte-Carlo process to process data file sequentially.\n",
    "- Specifiy the data file collection that will be load and analyzed sequentially.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "610763bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.optimize as opt\n",
    "from scipy.optimize import curve_fit\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import time\n",
    "%config InlineBackend.figure_format='retina'\n",
    "from concurrent.futures import ThreadPoolExecutor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69d20b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rho_sub=0.334 # water or 1mM K \n",
    "# rho_sub=0.3380936 # K2CO3 100 mM\n",
    "rho_sub=0.33440936 # K2CO3 10 mM\n",
    "\n",
    "# NSLS-II 12ID SMI x-ray energy, E_APS\n",
    "E_APS=9.7 # 10 keV x-ray energy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71566d50",
   "metadata": {},
   "source": [
    "## Step 1 : Initialize the methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24702ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x-ray energy: 9.7 keV\n",
       "x-ray wavelength : 1.2781443298969073 Å \n",
       "Subphase electron density : 0.33440936 e/Å³ \n",
       "Critical Qz : 0.0218 1/Å\n",
       "Core algorithm : Parratt's recursive method\n",
       "slabs of thickness 1.0 Å to approximate ED profile"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: set normalize=False will fit non-normalized reflectivity\n",
    "import reflec_calc_lib2 as ref\n",
    "\n",
    "Model =  ref.ReflecModel(9.7,\n",
    "                         rho_sub,\n",
    "                         d_slab=1.0,\n",
    "                         normalize=False)\n",
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b09371d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fitRef = Model.fitReflec # Working horse of the fitting function\n",
    "fitRF = Model.RF_calc # calculate the Fresnel reflectivity \n",
    "fitRho = Model.renderEDprofile # Generate ED profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc20aecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getChisq(fitfun, param, x, y, yerr):\n",
    "    yfit = fitfun(x, *param)\n",
    "    return np.sqrt(np.sum(((yfit-y)/yerr)**2))/(len(x)-len(param))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e96fc27",
   "metadata": {},
   "source": [
    "## Step 2 : Specify the parameter bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4d67c2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bound of each parameter\n",
    " \n",
    "I0_min=0.95; I0_max=1.05\n",
    "Qz_offset_min=-0.002; Qz_offset_max=0.002 \n",
    "sig0_min=3; sig0_max=10\n",
    "\n",
    "d1_min=5; d1_max=70  # width of 1st box\n",
    "rho1_min=rho_sub; rho1_max=0.8 # height(electron density) of 1st box\n",
    "sig1_min=1; sig1_max=15 # roughness of the layer-1 bottom interface\n",
    "\n",
    "d2_min=5; d2_max=70 # width of 2nd box\n",
    "rho2_min=rho_sub; rho2_max=0.8 # height(electron density) of 2nd box\n",
    "sig2_min=1; sig2_max=15 # roughness of the layer-1 bottom interface\n",
    "\n",
    "# d3_min=5; d3_max=50 # width of 3rd box\n",
    "# rho3_min=rho_sub; rho3_max=0.8 # height(electron density) of 3rd box\n",
    "# sig3_min=1; sig3_max=15  # roughness of the layer-1 bottom interface\n",
    "\n",
    "# d4_min=10; d4_max=150 # width of 4th box\n",
    "# rho4_min=rho_sub; rho4_max=0.6 # height(electron density) of 3rd box\n",
    "# sig4_min=3; sig4_max=50  # roughness of the layer-1 bottom interface\n",
    "\n",
    "param_lb=np.array([I0_min, Qz_offset_min, sig0_min,\n",
    "          d1_min, rho1_min, sig1_min,\n",
    "          d2_min, rho2_min, sig2_min, \n",
    "          # d3_min, rho3_min, sig3_min,\n",
    "          # d4_min, rho4_min, sig4_min,\n",
    "          ])\n",
    "\n",
    "param_ub=np.array([I0_max, Qz_offset_max, sig0_max,\n",
    "          d1_max, rho1_max, sig1_max, \n",
    "          d2_max, rho2_max, sig2_max,\n",
    "          # d3_max, rho3_max, sig3_max,\n",
    "          # d4_max, rho4_max, sig4_max,\n",
    "          ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8926e25a-c705-49b8-a89b-488323ebc4ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7222f419",
   "metadata": {},
   "source": [
    "## Step 3: Define a Monte-Carlo simulation process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "12df8de9-10d9-4840-9c2a-843bd36a90d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_combined(x, yn, xfit, yfit, z, rhoRef, base_filename, output_dir):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "    # Plot experimental fit\n",
    "    ax1.plot(x, yn, 'o', markersize=8, label='Experimental Data', color='red', alpha=0.6)\n",
    "    ax1.plot(xfit, yfit, '-', lw=3, label='Fit')\n",
    "    ax1.set_yscale('log')\n",
    "    ax1.set_xlabel(r'$Q_z$', fontsize=20)\n",
    "    ax1.set_ylabel(r'$R/RF$', fontsize=20)\n",
    "    ax1.tick_params(axis='both', labelsize=9)\n",
    "    ax1.set_title('Reflectivity Fitting',fontsize=20)\n",
    "    ax1.legend()\n",
    "\n",
    "    # Plot electron density\n",
    "    ax2.plot(z, rhoRef, '-', lw=3, color='k')\n",
    "    ax2.set_ylim(-0.05, 0.8)\n",
    "    ax2.set_ylabel(r'$\\rho$ $[e/A^3]$', fontsize=20)\n",
    "    ax2.set_xlabel('z [A]', fontsize=20)\n",
    "    ax2.set_title('Electron Density',fontsize=20)\n",
    "\n",
    "    fig.tight_layout()\n",
    "    # Save the combined figure\n",
    "    fig_file = os.path.join(output_dir, f'{base_filename}_combined_fig.png')\n",
    "    fig.savefig(fig_file, dpi=300)\n",
    "    print(f'Saved the figure to: {fig_file}')\n",
    "    plt.show()\n",
    "\n",
    "def monte_carlo_process(data_path, output_dir, filename, loop_number=100):\n",
    "    \"\"\"Monte-Carlo style of data fitting for one data set\"\"\"\n",
    "    # Get x, y, yerr from filename\n",
    "    data = pd.read_csv(data_path, sep='\\s+',names=['qz','ref','error'])\n",
    "    x = data['qz'][0:]\n",
    "    y = data['ref'][0:]\n",
    "    yerr = data['error'][0:]\n",
    "\n",
    "    # Create base output filename\n",
    "    base_filename = os.path.splitext(filename)[0]\n",
    "\n",
    "    # Strip off the .txt extension if present\n",
    "    if base_filename.endswith('.txt'):\n",
    "        base_filename = base_filename[:-4]\n",
    "\n",
    "    # determine the output file name to save parameters\n",
    "    param_file = os.path.join(output_dir, f'{base_filename}_params.csv')\n",
    "\n",
    "    # Loop for random trial of fitting (Monte Carlo part)\n",
    "    chisq_min = 1e6  # a very large number\n",
    "    np.random.seed(1)\n",
    "    record_collection = []\n",
    "    for count in range(loop_number):\n",
    "        print(\"# {} fitting ...\".format(count))\n",
    "\n",
    "        c = np.random.rand(len(param_lb))\n",
    "        p0 = param_lb + (param_ub - param_lb) * c\n",
    "        t0 = time.time()\n",
    "        try:\n",
    "            param_opt, param_cov = curve_fit(fitRef, x, y, p0, sigma=yerr,\n",
    "                                             bounds=(param_lb, param_ub),\n",
    "                                             maxfev=1000)\n",
    "            t1 = time.time()\n",
    "            print('Elapse time :', t1 - t0)\n",
    "\n",
    "            chisq = getChisq(fitRef, param_opt, x, y, yerr)\n",
    "            if chisq <= chisq_min:\n",
    "                chisq_min = chisq\n",
    "                param_best = param_opt\n",
    "\n",
    "            print('chisq for this #{} run is : {}'.format(count, chisq),\n",
    "                  end='  ')\n",
    "            print(\"chisq best now is :\", chisq_min)\n",
    "            print('-' * 20)\n",
    "            record = {'attempt #': count, 'chisq': chisq, 'params': param_opt}\n",
    "            record_collection.append(record)\n",
    "        except RuntimeError as e:\n",
    "            print(\"RuntimeError :\", e)\n",
    "    print(\"Finally, best parameters are :------\\n\", param_best)\n",
    "\n",
    "    df = pd.DataFrame(record_collection)\n",
    "    df.sort_values(by='chisq', inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    df.to_csv(param_file, index=False)\n",
    "    print('Did save the params to :', param_file)\n",
    "\n",
    "    # Additional Plots\n",
    "    xfit = np.linspace(np.min(x), np.max(x), 200)\n",
    "    popt = param_best\n",
    "    xerr = popt[1]\n",
    "    y_RF = fitRF(x - xerr)\n",
    "    y_RF_fit = fitRF(xfit - xerr)\n",
    "    yfit = fitRef(xfit, *popt) / y_RF_fit\n",
    "\n",
    "    yn = y / y_RF\n",
    "    yn_err = yerr / y_RF\n",
    "\n",
    "    # Save DataFrame to a .csv file\n",
    "    df_exp = pd.DataFrame({'x': x, 'yn': yn})\n",
    "    df_fit = pd.DataFrame({'xfit': xfit, 'yfit': yfit})\n",
    "\n",
    "    df_exp_file = os.path.join(output_dir, f'{base_filename}_rrf.csv')\n",
    "    df_fit_file = os.path.join(output_dir, f'{base_filename}_rrf_fit.csv')\n",
    "    df_exp.to_csv(df_exp_file, index=False)\n",
    "    df_fit.to_csv(df_fit_file, index=False)\n",
    "\n",
    "    print(f'Saved experimental data to: {df_exp_file}')\n",
    "    print(f'Saved fit data to: {df_fit_file}')\n",
    "\n",
    "    p = popt\n",
    "    z=np.arange(-300,50,1)\n",
    "    rhoRef = fitRho(z, *p)\n",
    "\n",
    "    # Create a DataFrame from z and rhoRef\n",
    "    df_rho = pd.DataFrame({'z': z, 'rho': rhoRef})\n",
    "\n",
    "    df_rho_file = os.path.join(output_dir, f'{base_filename}_ED.csv')\n",
    "    df_rho.to_csv(df_rho_file, index=False)\n",
    "\n",
    "    print(f'Saved electron density data to: {df_rho_file}')\n",
    "\n",
    "    # Combine and plot experimental fit and electron density\n",
    "    plot_combined(x, yn, xfit, yfit, z, rhoRef, base_filename, output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19329af3-003d-43ef-8dad-5072428e6fc3",
   "metadata": {},
   "source": [
    "### old code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5393931e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def monte_carlo_process(data_path, output_path, loop_number=100):\n",
    "#     \"\"\"Monte-Carlo style of data fitting for one data set\n",
    "#     \"\"\"\n",
    "#     # Get x, y, yerr from filename\n",
    "#     data = pd.read_csv(data_path, sep='\\s+')   \n",
    "#     x = data['qz'][2:]\n",
    "#     y = data['ref'][2:]\n",
    "#     yerr = data['error'][2:]\n",
    "#     # determine the output file name to save parameters\n",
    "#     param_file = output_path\n",
    "    \n",
    "#     # Loop for random trial of fitting (Monte Carlo part)\n",
    "#     chisq_min=1e6 # a very large number \n",
    "#     np.random.seed(1)\n",
    "#     record_collection=[]\n",
    "#     for count in range(loop_number):\n",
    "#         print(\"# {} fitting ...\".format(count))\n",
    "\n",
    "#         c=np.random.rand(len(param_lb))\n",
    "#         p0=param_lb+(param_ub-param_lb)*c\n",
    "#         t0=time.time()\n",
    "#         try:\n",
    "#             param_opt, param_cov=curve_fit(fitRef, x,\n",
    "#                                            y, p0, sigma=yerr, \n",
    "#                                            bounds=(param_lb,param_ub),\n",
    "#                                            maxfev = 1000)\n",
    "#             t1=time.time()\n",
    "#             print('Elapse time :', t1-t0)\n",
    "    \n",
    "#             chisq=getChisq(fitRef, param_opt, x, \n",
    "#                            y, yerr)\n",
    "#             if chisq<=chisq_min:\n",
    "#                 chisq_min=chisq\n",
    "#                 param_best=param_opt\n",
    "        \n",
    "#             print('chisq for this #{} run is : {}'.format(count ,chisq), \n",
    "#               end='  ')\n",
    "#             print(\"chisq best now is :\", chisq_min)\n",
    "#             print('-'*20)\n",
    "#             record={'attemp #': count, 'chisq':chisq, 'params':param_opt}\n",
    "#             record_collection.append(record)\n",
    "#         except RuntimeError as e:\n",
    "#             print(\"RuntimeError :\", e)\n",
    "#     print(\"Finally, best parameters is :------\\n\",param_best)\n",
    "     \n",
    "#     df=pd.DataFrame(record_collection)\n",
    "#     df.sort_values(by='chisq',inplace=True)\n",
    "#     df.reset_index(inplace=True)\n",
    "#     df.to_csv(param_file, index=False)\n",
    "#     print('Did save the params to :', param_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbdca8a",
   "metadata": {},
   "source": [
    "## Step 4 :  Specify the conditions that need to run overnight\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e8b3f309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R-water2-2e8300bc.txt',\n",
       " 'R-PEG2k_AgNP10_10mM_K-2910a595.txt',\n",
       " 'R-PEG5k_AgNP10_10mM_K-b765e504.txt',\n",
       " 'R-PEG20k_AgNP10_10mM_K-26bfde93.txt',\n",
       " 'R-PEG2k_AgNP10_0mM_PAA-d3b5980c.txt',\n",
       " 'R-PEG5k_AgNP10_100mM_K-f29435bd.txt',\n",
       " 'R-PEG2k_AgNP10_0.2mM_PAA-aca5b695.txt',\n",
       " 'R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205.txt',\n",
       " 'R-PEG5k_AgNP10_0mM_PAA-6df89ea8.txt',\n",
       " 'R-PEG5k_AgNP10_0.2mM_PAA-38a03ec7.txt',\n",
       " 'R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3.txt',\n",
       " 'R-PEG40k_AgNP10_0mM_PAA-10c56049.txt',\n",
       " 'R-PEG40k_AgNP10_0.2mM_PAA-5eff49f8.txt',\n",
       " 'R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544.txt',\n",
       " 'R-PEG5k_AuNP5_10mM_K-74115e32.txt',\n",
       " 'R-PEG2k_AgNP10_2mM_PAA-18f54ef1.txt',\n",
       " 'R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353.txt']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"C:\\\\Users\\\\binay\\\\Box\\\\APS_Ag_Binary\\\\New_BNL_replacement\\\\data2\\\\\"\n",
    "# path_1=\"C:\\\\Users\\\\binay\\\\Box\\\\APS_Ag_Binary\\\\Analysis\\\\SPRRF\\\\\"\n",
    "name_tags = ['R'\n",
    "            # 'PEG2k',\n",
    "            #  'PEG5k',\n",
    "             # '10mM',\n",
    "             # '.txt',\n",
    "            ]\n",
    "\n",
    "file_names = glob.glob(path+'*'+'*'.join(name_tags)+'*')\n",
    "file_names.sort(key=os.path.getmtime)\n",
    "\n",
    "names = [os.path.basename(file) for file in file_names]\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "348f03da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.listdir(os.path.join('Reflec data','wih HCl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b4aeff0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R_1to1': 'R-PEG2k_AgNP10_10mM_K-2910a595.txt',\n",
       " 'R_1to2': 'R-PEG5k_AgNP10_10mM_K-b765e504.txt',\n",
       " 'R_1to4': 'R-PEG20k_AgNP10_10mM_K-26bfde93.txt'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames = {\n",
    "    'R_1to1': 'R-PEG2k_AgNP10_10mM_K-2910a595.txt',\n",
    "    'R_1to2': 'R-PEG5k_AgNP10_10mM_K-b765e504.txt',\n",
    "    'R_1to4': 'R-PEG20k_AgNP10_10mM_K-26bfde93.txt',\n",
    "}\n",
    "\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d41e9515-f3a7-418d-8d6f-91b347ca9e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_csv(path+'T2-PEG2k-Ag10-1to1-PEG5k-Au5-K2CO3-1mM-scan598-601_ref.txt', sep='\\s+')\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "29ab090e-0123-439a-bca1-1179df9dd2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "69b89313",
   "metadata": {},
   "outputs": [],
   "source": [
    "conds = [ 'R_1to1','R_1to2','R_1to4']\n",
    "path = path  # directory where data files are located\n",
    "output_dir = './results1'  # directory where output files will be saved\n",
    "\n",
    "# Create the output directory if it does not exist\n",
    "os.makedirs(output_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7d0f9bac-61e5-4c55-886c-cea5a372d527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 0 fitting ...\n",
      "# 0 fitting ...\n",
      "# 0 fitting ...\n"
     ]
    }
   ],
   "source": [
    "def process_condition(cond):\n",
    "    filename = filenames[cond]\n",
    "    data_path = os.path.join(path, filename)\n",
    "    monte_carlo_process(data_path, output_dir, filename, 100)\n",
    "    print(f\"{cond} is done\")\n",
    "\n",
    "# Use ThreadPoolExecutor to parallelize the processing in Jupyter\n",
    "with ThreadPoolExecutor() as executor:\n",
    "    executor.map(process_condition, conds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c6feb69-f929-457a-962c-713cf8f3d1e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5dc38aeb-5ef3-4461-ad18-bbb4032b3092",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Additional plotting code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e87c4fb2-940e-47a1-a27a-e21e6755a987",
   "metadata": {},
   "outputs": [],
   "source": [
    "# popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "996c3b0e-91f8-4845-a764-bf1309037936",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv( os.path.join(path_1,'results1', 'T2-PEG2k-Ag10-1to1-PEG5k-Au5-K2CO3-100mM-scan638-641_ref' + '_params.csv'))\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "94217ad3-997a-42aa-adff-d81d0908773f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.68210990e-01, -1.22467193e-03,  1.00000000e+01,  4.41715204e+01,\n",
       "        3.38093600e-01,  2.68972969e+00,  1.06553996e+01,  6.54370153e-01,\n",
       "        7.09229182e+00,  3.39940458e+01,  5.92837983e-01,  1.16437818e+01])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbest = df['params'][0]\n",
    "pbest\n",
    "# Remove the newline characters and split the string into individual elements\n",
    "pbest = pbest.replace('\\n', '').replace('[', '').replace(']', '').split()\n",
    "\n",
    "# Convert the elements into a NumPy array of floats\n",
    "pbest = np.array(pbest, dtype=float)\n",
    "pbest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "56be1c2e-3a1d-4b26-b550-f6187ea3153e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(path+'T2-PEG2k-Ag10-1to1-PEG5k-Au5-K2CO3-100mM-scan638-641_ref.txt', sep='\\s+',names=['qz','ref','error'])  \n",
    "x = data['qz'][0:]\n",
    "y = data['ref'][0:]\n",
    "yerr = data['error'][0:]\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "57b1db29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 697,
       "width": 1489
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit=np.linspace(np.min(x),np.max(x),200)\n",
    "yfit=fitRef(xfit, *pbest) \n",
    "\n",
    "fig, ax =plt.subplots(nrows=1, ncols=2, figsize=(18,8));\n",
    "ax[0].errorbar(x, y, yerr=yerr,fmt='o',markersize=10,\n",
    "              markerfacecolor='w',alpha=0.8);\n",
    "ax[0].plot(xfit, yfit, '-', color='r', \n",
    "           lw=3,label='data');\n",
    "ax[0].set_yscale('log')\n",
    "ax[0].set_ylabel('R',fontsize=20)\n",
    "ax[0].set_xlabel('Qz [1/A]', fontsize=20)\n",
    "\n",
    "z=np.arange(-300,50,1)\n",
    "rho=fitRho(z, *pbest)\n",
    "ax[1].plot(z, rho,'-',lw=3, color='k');\n",
    "ax[1].set_ylim(-0.05,0.6)\n",
    "ax[1].set_ylabel(r'$\\rho$ [e/A3]',fontsize=20)\n",
    "ax[1].set_xlabel('z [A]',fontsize=20)\n",
    "# p=popt.copy()\n",
    "# p[2::3]=0.0001\n",
    "rho_rect = fitRho(z, *pbest)\n",
    "# ax[1].plot(z, rho_rect,'--',lw=3, color='b');\n",
    "# ax[1].set_ylim(-0.05,0.6)\n",
    "# ax[1].set_ylabel(r'$\\rho$ [e/A3]',fontsize=20)\n",
    "# ax[1].set_xlabel('z [A]',fontsize=20)\n",
    "#do not need boxes for figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2bfd2048-fa15-4acd-b65b-acbf6c35126b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\binay\\\\Box\\\\APS_Ag_Binary\\\\Analysis\\\\SPRRF'"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "2cb3c553-e215-4589-a98f-59fc8eeaad97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 370,
       "width": 381
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(np.min(x), np.max(x), 200)\n",
    "popt = pbest\n",
    "xerr = popt[1]\n",
    "y_RF = fitRF(x - xerr)\n",
    "y_RF_fit = fitRF(xfit - xerr)\n",
    "yfit = fitRef(xfit, *popt) / y_RF_fit\n",
    "\n",
    "yn = y / y_RF\n",
    "yn_err = yerr / y_RF\n",
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'x': x,\n",
    "    'yn': yn\n",
    "})\n",
    "\n",
    "df2 = pd.DataFrame({\n",
    "    'xfit': xfit,\n",
    "    'yfit': yfit\n",
    "})\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "ax.plot(x, yn, 'o', markersize=8, label='Experimental Data', color='red', alpha=0.6)\n",
    "ax.plot(xfit, yfit, '-', lw=3, label='Fit')\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(r'$Q_z$', fontsize=10)\n",
    "ax.set_ylabel(r'$xR/RF$', fontsize=10)\n",
    "ax.tick_params(axis='both', labelsize=9)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f37c7319-4eed-4a11-8012-d17a9bd2303c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.815647694276615\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 451,
       "width": 582
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = popt\n",
    "p[4::3] = 0.334\n",
    "rhoRef = fitRho(z, *p)\n",
    "rr = np.sum(rho - rhoRef)\n",
    "\n",
    "# Create a DataFrame from z and rhoRef\n",
    "df = pd.DataFrame({'z': z, 'rho': rho})\n",
    "\n",
    "plt.plot(z, rhoRef,'-',lw=3, color='k');\n",
    "plt.plot(z, rho, '-', lw=3, color='red')\n",
    "plt.ylim(-0.05,0.6)\n",
    "plt.ylabel(r'$\\rho$ [e/A3]',fontsize=20)\n",
    "plt.xlabel('z [A]',fontsize=20)\n",
    "print(rr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c66db52f-2bf4-4a9b-934f-3d76fbb91b30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
